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Found 12 papers, 6 papers with code
Tidying Deep Saliency Prediction Architectures
As a result, we propose two novel end-to-end architectures called SimpleNet and MDNSal, which are neater, minimal, more interpretable and achieve state of the art performance on public saliency benchmarks.
Price Optimization in Fashion E-commerce
Thus we obtain multiple price demand pairs for each product and we have to choose one of them for the live platform.
Towards Multimodal MIR: Predicting individual differences from music-induced movement
As the field of Music Information Retrieval grows, it is important to take into consideration the multi-modality of music and how aspects of musical engagement such as movement and gesture might be taken into account.
Exploiting Temporal Attention Features for Effective Denoising in Videos
Video Denoising is one of the fundamental tasks of any videoprocessing pipeline.
ViNet: Pushing the limits of Visual Modality for Audio-Visual Saliency Prediction
We also explore a variation of ViNet architecture by augmenting audio features into the decoder.
Towards Achieving Adversarial Robustness Beyond Perceptual Limits
The presence of images that flip Oracle predictions and those that do not, makes this a challenging setting for adversarial robustness.
Scaling Adversarial Training to Large Perturbation Bounds
The presence of images that flip Oracle predictions and those that do not makes this a challenging setting for adversarial robustness.
Efficient and Effective Augmentation Strategy for Adversarial Training
We first explain this contrasting behavior by viewing augmentation during training as a problem of domain generalization, and further propose Diverse Augmentation-based Joint Adversarial Training (DAJAT) to use data augmentations effectively in adversarial training.
DART: Diversify-Aggregate-Repeat Training Improves Generalization of Neural Networks
Generalization of neural networks is crucial for deploying them safely in the real world.
Boosting Adversarial Robustness using Feature Level Stochastic Smoothing
Advances in adversarial defenses have led to a significant improvement in the robustness of Deep Neural Networks.
Knowledge Graph Representations to enhance Intensive Care Time-Series Predictions
Intensive Care Units (ICU) require comprehensive patient data integration for enhanced clinical outcome predictions, crucial for assessing patient conditions.
Mechanistically analyzing the effects of fine-tuning on procedurally defined tasks
Fine-tuning large pre-trained models has become the de facto strategy for developing both task-specific and general-purpose machine learning systems, including developing models that are safe to deploy.
